A Virtual Reality Environment for Genomic Data Visualization

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DESCRIPTION (provided by applicant): This project will develop a new virtual reality display environment for visualization of multi-channel, multi- dimensional data from genomic experiments. The advent of genomics and DNA micro arrays has changed the situation in research drastically. Researchers now must sift through reams of experimental data to detect relationships among groups of genes. Therefore, new data visualization tools are required to support modern science. Our approach is to combine statistical data projection with virtual reality (VR) display technology in a flexible way that projects N-dimensional data (3=N) to M dimensions (3=M=10), and displays it to the investigator in a 3-D virtual space. A unique feature of the proposed system is that it will provide the investigator the option to select among a number of data projection techniques, including principal component analysis (PCA), linear discriminant analysis (LDA), singular value decomposition (SVD), multi-dimensional scaling (MDS), projection pursuit (PP), self-organizing maps (SOMs), independent component analysis (ICA), and others. Data will be displayed as iconic objects floating in space that differ in size, shape, color, texture, position, orientation and spin. The VR display will be implemented as a workstation-based fly-through environment in this Phase I study. The aim of the Phase I project is to demonstrate clearly the value of VR- based data visualization tools in genomic data analysis. If Phase I is successful, a VR walk-through immersive environment will be developed and evaluated in Phase II. The resulting commercial hardware/software package will provide significantly improved scientific data visualization and interpretation to assist genomics, proteomics and bioinformatics researchers, and pattern recognition system developers in the interpretation of their overwhelmingly complex experimental data. The resulting commercial hardware/software package will provide significantly improved scientific data visualization and interpretation to assist genomics, proteomics and bioinformatics researchers, and pattern recognition system developers in the interpretation of their overwhelmingly complex experimental data. The primary healthcare benefits that will accrue from this development will result from the accelerated development of diagnostics and treatments for diseases.